SIB Days 2020 - Virtual Conference - June 8, 2020
Dataset: Breast Cancer
Patrick Roelli, Computational Biologist 2 - Computational Biology1
Stefania Giacomello, Computational Biologist 2 - Computational Biology2
Stephen Williams, Senior Scientist - Computational Biology3
Last Compiled: May 29, 2020
Introduction
Welcome to the SIB Days 2020 - virtual conference Spatial Transcriptomics workshop by 10x genomics!
The purpose of this tutorial will be to walk users through some of the steps necessary to explore data produced by the 10x Genomics Visium Spatial Gene Expression Solution and the Spaceranger pipeline. We will investigate the data sets are all freely available from 10x Genomics.
Things to know about this workshop
- All files that will be used can be found at:
/mnt/libs/shared_data/
[
]
- Getting started with R and Visium data outside of Seurat at: https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/rkit
- Reference genome for all samples is GRCh38/mm10
- All 10x software including Spaceranger, and Loupe Browser, can be downloaded from the 10x Support Site
- Most Seurat figures can be controlled with patchwork because they are fundamentally ggplot objects. Some really nice examples can be found on the Patchwork GitHub
Exploring Visium Data with Seurat
Load our packages
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(RColorBrewer)Loading data in a Seurat object
Real Dataset for the tutorial
breast_cancer <- Load10X_Spatial(data.dir = "/mnt/libs/shared_data/human_breast_cancer_1/outs/",
filename = "V1_Breast_Cancer_Block_A_Section_1_filtered_feature_bc_matrix.h5")Same data just internal to 10x
breast_cancer <- Load10X_Spatial(data.dir = "/mnt/analysis/marsoc/pipestances/HWHTFDSXX/SPATIAL_RNA_COUNTER_PD/163086/HEAD/outs/", slice = "slice1")There are a bunch of data sets hosted by the Satija lab in the Seurat Data Package.
Results
QC
Let’s have a look at some basic QC information. Keep in mind that most Seurat plots are ggplot object and can be manipulated as such.
Counts = UMI Features = Genes
plot1 <- VlnPlot(breast_cancer, features = "nCount_Spatial", pt.size = 0.1) +
ggtitle("UMI") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "right") +
NoLegend()
plot2 <- VlnPlot(breast_cancer, features = "nFeature_Spatial", pt.size = 0.1) +
ggtitle("Genes") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "right") +
NoLegend()
plot3 <- SpatialFeaturePlot(breast_cancer, features = "nCount_Spatial") +
theme(legend.position = "right")
plot4 <- SpatialFeaturePlot(breast_cancer, features = "nFeature_Spatial") +
theme(legend.position = "right")
plot1 + plot2 + plot3 + plot4 + plot_layout(nrow = 2, ncol = 2)Normalization
Spaceranger does normalization for clustering and DE but does not return that normalized matrix
Pre-normalization Raw UMI counts
SpatialFeaturePlot(breast_cancer, features = c("ERBB2", "CD8A"))
SE transform
- This will take ~3-4 min.
Don’t worry about reachediteration limit warnings. See https://github.com/ChristophH/sctransform/issues/25 for discussion
Default assay will now be set to SCT
breast_cancer <- SCTransform(breast_cancer, assay = "Spatial", verbose = TRUE)Calculating cell attributes for input UMI matrix
Variance stabilizing transformation of count matrix of size 19751 by 3822
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 3822 cells
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|===========================================================================| 100%Found 2 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 19751 genes
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|===========================================================================| 100%Computing corrected count matrix for 19751 genes
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|===========================================================================| 100%Calculating gene attributes
Wall clock passed: Time difference of 2.091646 mins
Determine variable features
Set 3000 variable features
Place corrected count matrix in counts slot
Centering data matrix
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Set default assay to SCTSpatialFeaturePlot(breast_cancer, features = c("ERBB2", "CD8A"))
From Seurat:
The default parameters in Seurat emphasize the visualization of molecular data. However, you can also adjust the size of the spots (and their transparency) to improve the visualization of the histology image, by changing the following parameters:
pt.size.factor- This will scale the size of the spots. Default is 1.6
alpha - minimum and maximum transparency. Default is c(1, 1).
Try setting to alpha c(0.1, 1), to downweight the transparency of points with lower expressionp1 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5", pt.size.factor = 1)+
theme(legend.position = "right") +
ggtitle("Actual Spot Size")
p2 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5")+
theme(legend.position = "right") +
ggtitle("Scaled Spot Size")
p1 + p2
Dimensionality reduction, clustering, and visualization
We can then proceed to run dimensionality reduction and clustering on the RNA expression data, using the same workflow as we use for scRNA-seq analysis.
Some of these processes can be parallized
library(future)
# check the current active plan
plan()# change the current plan to access parallelization
plan("multiprocess", workers = 4)
plan()The default UMAP calculation is performed with the R-based UWOT library However, you can run UMAP in python via reticulate library and umap-learn. We have found that for smaller data sets (<= 10k cells/spots) UWOT is great. For much larger data sets (100k + cells/spots) umap-learn can be a faster option.
breast_cancer <- RunPCA(breast_cancer, assay = "SCT", verbose = FALSE)
breast_cancer <- FindNeighbors(breast_cancer, reduction = "pca", dims = 1:30)Computing nearest neighbor graph
Computing SNNbreast_cancer <- FindClusters(breast_cancer, verbose = FALSE)
breast_cancer <- RunUMAP(breast_cancer, reduction = "pca", dims = 1:30)The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session11:22:48 UMAP embedding parameters a = 0.9922 b = 1.112
11:22:48 Read 3822 rows and found 30 numeric columns
11:22:48 Using Annoy for neighbor search, n_neighbors = 30
11:22:48 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:22:48 Writing NN index file to temp file /tmp/RtmpHLaSG1/file707f428273fa9
11:22:48 Searching Annoy index using 1 thread, search_k = 3000
11:22:49 Annoy recall = 100%
11:22:50 Commencing smooth kNN distance calibration using 1 thread
11:22:50 Initializing from normalized Laplacian + noise
11:22:54 Commencing optimization for 500 epochs, with 157848 positive edges
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[----|----|----|----|----|----|----|----|----|----|
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11:23:03 Optimization finishedNow let’s have a look at the clustering
DimPlot(breast_cancer, reduction = "umap", label = FALSE,) +
labs(color = "Cluster")
p1 <- DimPlot(breast_cancer, reduction = "umap", label = TRUE) +
labs(color = "Cluster")
p2 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
labs(fill = "Cluster")
p1 + p2 + plot_annotation(
title = 'Clustering in UMAP and Tissue Space',
caption = 'Processed by Spaceranger 1.1\nNormilization and Clustering by Seurat'
) + plot_layout(nrow = 1)
I don’t really like these colors so let’s change them
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))p1 <- DimPlot(breast_cancer, reduction = "umap", label = TRUE) +
labs(color = "Cluster") +
scale_color_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green"))
p2 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
labs(fill = "Cluster")+
scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green"))
p1 + p2 + plot_annotation(
title = 'Clustering in UMAP and Tissue Space',
caption = 'Processed by Spaceranger 1.1\nNormilization and Clustering by Seurat'
) + plot_layout(nrow = 1)
If interested you can also now look at UMI and Gene counts per cluster as well
plot1 <- VlnPlot(breast_cancer, features = "nCount_Spatial", pt.size = 0.1) +
ggtitle("UMI") +
scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green"))+
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "right") +
NoLegend()
plot2 <- VlnPlot(breast_cancer, features = "nFeature_Spatial", pt.size = 0.1) +
ggtitle("Genes") +
scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green"))+
theme(axis.title.x = element_blank(),
legend.position = "right") +
NoLegend()
plot1 + plot2
Now let’s take a look at at a gene of interest with violin plots but also in image space.
p1 <- VlnPlot(breast_cancer, features = "IGFBP5", pt.size = 0.1) +
ggtitle("IGFBP5") +
scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green"))+
theme(axis.title.x = element_blank(),
legend.position = "right") +
NoLegend() +
stat_summary(fun=mean, geom="point", shape=23, size=4, color="red")
p2 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5")+
theme(legend.position = "right")CombinePlots is being deprecated. Plots should now be combined using the patchwork system.p3 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
labs(fill = "Cluster")+
scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold",
"#a65628", "#999999", "black", "pink", "purple", "brown",
"grey", "yellow", "green")) +
NoLegend()CombinePlots is being deprecated. Plots should now be combined using the patchwork system.row1 <- p2 + p3 + plot_layout(nrow = 1)
row1 + p1+ plot_layout(nrow = 2, widths = c(0.5, 0.5))
We can also look at these data interactively
LinkedDimPlot(breast_cancer)Spatially variable features
First we’ll identify deferentially expressed genes.
Parallelization helps here too let’s make sure our plan is still intact
plan()Error in plan() : could not find function "plan"- call: plan("multiprocess", workers = 4) indicates that it is
Looks like we have some very DE genes for clusters 4 and 11
clarify what ident.1 = 4, ident.2 = 6 are for
de_markers <- FindMarkers(breast_cancer, ident.1 = 4, ident.2 = 6)
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de_markers_up <- de_markers %>%
tibble::rownames_to_column(var = "Gene") %>%
arrange(-avg_logFC)
de_markers_down <- de_markers %>%
tibble::rownames_to_column(var = "Gene") %>%
arrange(avg_logFC)Most up-regulated genes
SpatialFeaturePlot(object = breast_cancer, features = de_markers_up$Gene[1:10], alpha = c(0.1, 1), ncol = 3)
Most down-regulated genes
SpatialFeaturePlot(object = breast_cancer, features = de_markers_down$Gene[1:10], alpha = c(0.1, 1), ncol = 3)
what are the top variable features?
VariableFeatures(breast_cancer)[1:10] [1] "IGKC" "IGHG3" "IGLC2" "MGP" "ALB" "IGHG1" "CPB1" "CRISP3" "IGLC3"
[10] "IGHM" what are the top DE genes?
rownames(de_markers)[1:10] [1] "CRISP3" "IGFBP5" "SLITRK6" "H3F3A" "IGFBP4" "MALAT1" "C3" "KRT18"
[9] "S100A11" "MGP" Spatially Variable Genes
So what about spatial enrichment?
Some methods
- We have found this implementation not to be very effective. It’s also not under active development
Using the top 100 variable genes find spatially enriched ones. Note that in the Seurat Spatial Tutorial they use 1000 genes. You can also use all genes but that will take a long time. Using a calculation of Morans I can sometimes be a faster approach, especially if you are using parallization.
breast_cancer <- FindSpatiallyVariableFeatures(breast_cancer,
assay = "SCT",
slot = "scale.data",
features = VariableFeatures(breast_cancer)[1:100],
selection.method = "markvariogram", verbose = TRUE)Have a look at the spatially variable genes calculated by markvariogram ordered from most variable to least variable
SpatiallyVariableFeatures(breast_cancer, selection.method = "markvariogram", decreasing = TRUE) [1] "CRISP3" "CXCL14" "MGP" "CPB1" "COX6C" "SLITRK6"
[7] "TTLL12" "CCND1" "MALAT1" "AGR2" "ALB" "GFRA1"
[13] "S100G" "CSTA" "DEGS1" "TFF3" "MT-ND1" "IGLC2"
[19] "MT-CO1" "IGHG3" "CD74" "C6orf141" "S100A6" "MT-ND2"
[25] "TFF1" "IGKC" "IGHG1" "APOE" "ZNF350-AS1" "HLA-DRA"
[31] "AC087379.2" "IGHG4" "C3" "FCGR3B" "TIMP1" "LINC00645"
[37] "IGHM" "SCGB2A2" "KRT14" "IGLC3" "KRT17" "LYZ"
[43] "APOC1" "SCGB1D2" "IGHA1" "C1QA" "AEBP1" "APOD"
[49] "KRT5" "MMP7" "CCL19" "COL6A2" "TAGLN" "S100A9"
[55] "IGHG2" "COL1A2" "DCN" "SPP1" "COL1A1" "CGA"
[61] "VIM" "IGFBP7" "FN1" "CCDC80" "CXCL9" "IGHA2"
[67] "TRBC2" "SFRP2" "KRT6B" "S100A2" "LUM" "COL3A1"
[73] "IGLC7" "SAA1" "CARTPT" "COMP" "JCHAIN" "CST1"
[79] "PTGDS" "SFRP4" "CD79A" "CCL21" "FABP4" "MUC19"
[85] "ACKR1" "POSTN" "MMP9" "S100A7" "VWF" "AQP1"
[91] "CLDN5" "ACTA2" "MS4A1" "IGLL5" "MYH11" "CXCL10"
[97] "IGHD" "HBB" "TPSB2" "AC005224.3"top.features_trendseq <- head(SpatiallyVariableFeatures(breast_cancer, selection.method = "markvariogram"), 8)
SpatialFeaturePlot(breast_cancer, features = top.features_trendseq, ncol = 4, alpha = c(0.1, 1))
Moran’s I implementation. For other spatial data types the x.cuts and y.cuts determines the grid that is laid over the tissue in the capture area. Here we’ll remove those
breast_cancer <- FindSpatiallyVariableFeatures(breast_cancer,
assay = "SCT",
slot = "scale.data",
features = VariableFeatures(breast_cancer)[1:100],
selection.method = "moransi")Computing Moran's IHave a look at the spatially variable genes calculated by moransi ordered from most variable to least variable
SpatiallyVariableFeatures(breast_cancer, selection.method = "moransi", decreasing = TRUE) [1] "CRISP3" "CXCL14" "TTLL12" "SLITRK6" "GFRA1" "CCND1"
[7] "AGR2" "COX6C" "MGP" "ALB" "MALAT1" "CPB1"
[13] "DEGS1" "C6orf141" "CSTA" "MT-ND1" "TFF3" "MT-CO1"
[19] "S100A6" "MT-ND2" "LINC00645" "FCGR3B" "S100G" "TFF1"
[25] "C3" "ZNF350-AS1" "HLA-DRA" "CD74" "SCGB1D2" "APOD"
[31] "IGLC2" "TIMP1" "IGHG3" "SCGB2A2" "APOC1" "KRT14"
[37] "LYZ" "CCDC80" "APOE" "TAGLN" "IGHG1" "AC087379.2"
[43] "CCL19" "SPP1" "IGKC" "S100A9" "KRT17" "IGHM"
[49] "FN1" "KRT5" "IGFBP7" "C1QA" "COL6A2" "AQP1"
[55] "CXCL9" "ACKR1" "CARTPT" "DCN" "AEBP1" "IGLC3"
[61] "IGHG4" "VIM" "S100A2" "IGHG2" "MMP7" "IGHA1"
[67] "CCL21" "KRT6B" "TRBC2" "COL1A2" "CGA" "SFRP2"
[73] "VWF" "COL1A1" "SAA1" "MUC19" "COL3A1" "CLDN5"
[79] "SFRP4" "POSTN" "PTGDS" "IGHA2" "JCHAIN" "CD79A"
[85] "ACTA2" "LUM" "CST1" "S100A7" "COMP" "MS4A1"
[91] "IGLC7" "MMP9" "HBB" "FABP4" "MYH11" "IGLL5"
[97] "CXCL10" "TPSB2" "IGHD" "AC005224.3"top.features_moransi <- head(SpatiallyVariableFeatures(breast_cancer, selection.method = "moransi"), 8)
SpatialFeaturePlot(breast_cancer, features = top.features_moransi, ncol = 4, alpha = c(0.1, 1))
We can see that the results are slightly different. So let’s take a look at why.
spatially_variable_genes <- breast_cancer@assays$SCT@meta.features %>%
tidyr::drop_na()
spatially_variable_genesYou can see the two methods show
mm_cor <- cor.test(spatially_variable_genes$moransi.spatially.variable.rank, spatially_variable_genes$markvariogram.spatially.variable.rank)
ggplot(spatially_variable_genes, aes(x=moransi.spatially.variable.rank,y=markvariogram.spatially.variable.rank))+
geom_point()+
geom_smooth()+
xlab("Morans I Rank")+
ylab("Markvariogram Rank")+
annotate("text", x = 25, y = 75, label = paste("Pearson's Correlation\n", round(mm_cor$estimate[1], digits = 2), sep = ""))+
theme_bw()
Cancer annotations
Where are these genes being expressed relative to pathologist annotation?
ca <- readbitmap::read.bitmap("images/Breast Cancer Path.png")
# in the tutorial
# ca <- readbitmap::read.bitmap('/mnt/libs/shared_data/human_breast_cancer_1/images/Breast_Cancer_Path.png')
plot(0:1,0:1,type="n",ann=FALSE,axes=FALSE)
rasterImage(ca,0,0,1,1)
Looks like the Matrix Gla protein ( MGP ) gene is enriched in Ductal Carcinoma In Situ. Not a lot is known about MGP in the context of cancer but it looks like it could be an interesting novel gene to investigate with regard to Ductal Carcinoma In Situ.
SpatialFeaturePlot(object = breast_cancer, features = "MGP", alpha = c(0.1, 1), ncol = 3)
Multiple Sections
for workshop
breast_cancer_2 <- Load10X_Spatial(data.dir = "/mnt/libs/shared_data/human_breast_cancer_2/outs/",
filename = "V1_Breast_Cancer_Block_A_Section_2_filtered_feature_bc_matrix.h5")for 10x
breast_cancer_2 <- Load10X_Spatial(data.dir = "/mnt/analysis/marsoc/pipestances/HWHTFDSXX/SPATIAL_RNA_COUNTER_PD/163087/HEAD/outs/",
slice = "slice2")breast_cancer_merged <- merge(breast_cancer, breast_cancer_2)Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.Counts = UMI Features = Genes
plot1 <- SpatialFeaturePlot(breast_cancer_merged, features = "nCount_Spatial") +
theme(legend.position = "right")
plot2 <- SpatialFeaturePlot(breast_cancer_merged, features = "nFeature_Spatial") +
theme(legend.position = "right")
plot1 + plot2 + plot_layout(nrow = 2)
Normalize merged data
This will take ~3min
Default assay will now be set to SCT
breast_cancer_merged <- SCTransform(breast_cancer_merged, assay = "Spatial", verbose = TRUE)Calculating cell attributes for input UMI matrix
Variance stabilizing transformation of count matrix of size 20963 by 7834
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 7834 cells
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Determine variable features
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Set default assay to SCTSpatialFeaturePlot(breast_cancer_merged, features = c("ERBB2", "CD8A"))
Single cell/nuclei ingegration
Here we have a preprocessed Seurat object with 10k nuclei annotated from a breast cancer sample. Don’t bother too much with the details of how this data was generated they don’t particularly matter for our purposes.
Load the Seurat object
10x infrastructure
load("/mnt/home/stephen.williams/yard/Odin/SIB_2020_Workshop/bc_snRNA.rds")file ‘bc_snRNA.rds’ has magic number 'X'
Use of save versions prior to 2 is deprecatedError in load("/mnt/home/stephen.williams/yard/Odin/SIB_2020_Workshop/bc_snRNA.rds") :
bad restore file magic number (file may be corrupted) -- no data loadedFor the workshop
bc_snRNA <- readRDS("/mnt/libs/shared_data/bc_snRNA.rds")bc_snRNA
An object of class Seurat
61450 features across 10000 samples within 2 assays
Active assay: SCT (27912 features)
1 other assay present: RNA
3 dimensional reductions calculated: pca, harmony, umap
10x Genomics, patrick.roelli@10xgenomics.com ↩︎
10x Genomics, stefania.giacomello@10xgenomics.com↩︎
10x Genomics, stephen.williams@10xgenomics.com↩︎
---
title: "SIB Days 2020 - Virtual Conference - June 8, 2020"
subtitle: "Dataset: Breast Cancer"
author:
  - Patrick Roelli, Computational Biologist 2 - Computational Biology^[10x Genomics, patrick.roelli@10xgenomics.com ]
  - Stefania Giacomello, Computational Biologist 2 - Computational Biology^[10x Genomics, stefania.giacomello@10xgenomics.com]
  - Stephen Williams, Senior Scientist - Computational Biology^[10x Genomics, stephen.williams@10xgenomics.com]
date: 'Last Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
  html_notebook:
    code_folding: none
    theme: journal
    toc: yes
    toc_depth: 3
    toc_float: yes
---

[![10x Home](https://github.com/stephenwilliams22/SIB_2020_Workshop/raw/master/images/10x%20homepage.png)](https://www.10xgenomics.com/)

# **Introduction**

Welcome to the **SIB Days 2020 - virtual conference** Spatial Transcriptomics workshop by 10x genomics!

The purpose of this tutorial will be to walk users through some of the steps necessary to explore data produced by the 10x Genomics Visium Spatial Gene Expression Solution and the [Spaceranger pipeline](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger). We will investigate the data sets  are all freely available from [10x Genomics](https://support.10xgenomics.com/spatial-gene-expression/datasets).

[Seurat Tutorial](https://satijalab.org/seurat/v3.1/spatial_vignette.html)

**Things to know about this workshop**

1. All files that will be used can be found at: `/mnt/libs/shared_data/`

[![Terminal](https://github.com/stephenwilliams22/SIB_2020_Workshop/raw/master/images/Terminal.png)]

2. Getting started with R and Visium data outside of Seurat at: https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/rkit
3. Reference genome for all samples is GRCh38/mm10
4. All 10x software including [Spaceranger](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger), and [Loupe Browser](https://support.10xgenomics.com/spatial-gene-expression/software/visualization/latest/what-is-loupe-browser), can be downloaded from the [10x Support Site](https://support.10xgenomics.com/)
5. Most Seurat figures can be controlled with patchwork because they are fundamentally ggplot objects. Some really nice examples can be found on the [Patchwork GitHub](https://patchwork.data-imaginist.com/articles/guides/layout.html)


# **Exploring Visium Data with Seurat**

## Load our packages
```{r Libraries, echo=TRUE, message=FALSE, warning=FALSE}
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(RColorBrewer)
```

## Loading data in a Seurat object

Real Dataset for the tutorial
```{r eval=FALSE}
breast_cancer <- Load10X_Spatial(data.dir = "/mnt/libs/shared_data/human_breast_cancer_1/outs/",
                filename = "V1_Breast_Cancer_Block_A_Section_1_filtered_feature_bc_matrix.h5")
```


Same data just internal to 10x
```{r}
breast_cancer <- Load10X_Spatial(data.dir = "/mnt/analysis/marsoc/pipestances/HWHTFDSXX/SPATIAL_RNA_COUNTER_PD/163086/HEAD/outs/", slice = "slice1")
```

There are a bunch of data sets hosted by the Satija lab in the [Seurat Data Package](https://github.com/satijalab/seurat-data).

# **Results**
## QC
Let's have a look at some basic QC information. Keep in mind that most Seurat plots are ggplot object and can be manipulated as such.

Counts = UMI
Features = Genes
```{r, fig.width=10,fig.height=8, warning=FALSE}
plot1 <- VlnPlot(breast_cancer, features = "nCount_Spatial", pt.size = 0.1) + 
  ggtitle("UMI") +
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(), 
        legend.position = "right") +
  NoLegend()

plot2 <- VlnPlot(breast_cancer, features = "nFeature_Spatial", pt.size = 0.1) + 
  ggtitle("Genes") +
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(), 
        legend.position = "right") +
  NoLegend()

plot3 <- SpatialFeaturePlot(breast_cancer, features = "nCount_Spatial") + 
  theme(legend.position = "right")

plot4 <- SpatialFeaturePlot(breast_cancer, features = "nFeature_Spatial") +
  theme(legend.position = "right")

plot1 + plot2 + plot3  + plot4 + plot_layout(nrow = 2, ncol = 2)
```

## Normalization

Spaceranger does normalization for clustering and DE but does not return that normalized matrix

Pre-normalization 
Raw UMI counts
```{r, fig.width=10, warning=FALSE}
SpatialFeaturePlot(breast_cancer, features = c("ERBB2", "CD8A"))
```

### SE transform

+  This will take ~3-4 min. 

Don't worry about `reachediteration limit` warnings. See https://github.com/ChristophH/sctransform/issues/25 for discussion

Default assay will now be set to SCT
```{r, warning=FALSE}
breast_cancer <- SCTransform(breast_cancer, assay = "Spatial", verbose = TRUE)
```

```{r, fig.width=10, warning=FALSE}
SpatialFeaturePlot(breast_cancer, features = c("ERBB2", "CD8A"))
```


From Seurat: 

The default parameters in Seurat emphasize the visualization of molecular data. However, you can also adjust the size of the spots (and their transparency) to improve the visualization of the histology image, by changing the following parameters:

    pt.size.factor- This will scale the size of the spots. Default is 1.6
    alpha - minimum and maximum transparency. Default is c(1, 1).
    Try setting to alpha c(0.1, 1), to downweight the transparency of points with lower expression



```{r, fig.width=10, warning=FALSE}
p1 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5", pt.size.factor = 1)+ 
  theme(legend.position = "right") +
  ggtitle("Actual Spot Size")
p2 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5")+ 
  theme(legend.position = "right") +
  ggtitle("Scaled Spot Size")
p1 + p2
```

### Dimensionality reduction, clustering, and visualization

We can then proceed to run dimensionality reduction and clustering on the RNA expression data, using the same workflow as we use for scRNA-seq analysis.

Some of these processes can be parallized

```{r eval=FALSE, include=TRUE}
library(future)
# check the current active plan
plan()
```

```{r eval=FALSE, include=TRUE}
# change the current plan to access parallelization
plan("multiprocess", workers = 4)
plan()
```


The default UMAP calculation is performed with the [R-based UWOT](https://cran.r-project.org/web/packages/uwot/index.html) library However, you can run UMAP in python via reticulate library and `umap-learn`. We have found that for smaller data sets (<= 10k cells/spots) UWOT is great. For much larger data sets (100k + cells/spots) `umap-learn` can be a faster option.

```{r}
breast_cancer <- RunPCA(breast_cancer, assay = "SCT", verbose = FALSE)
breast_cancer <- FindNeighbors(breast_cancer, reduction = "pca", dims = 1:30)
breast_cancer <- FindClusters(breast_cancer, verbose = FALSE)
breast_cancer <- RunUMAP(breast_cancer, reduction = "pca", dims = 1:30)
```

Now let's have a look at the clustering

```{r, fig.width=8}
DimPlot(breast_cancer, reduction = "umap", label = FALSE,) +
  labs(color = "Cluster")
```

```{r, fig.width=10,fig.height=10, warning=FALSE}
p1 <- DimPlot(breast_cancer, reduction = "umap", label = TRUE) +
  labs(color = "Cluster")
p2 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
  labs(fill = "Cluster")

p1 + p2 + plot_annotation(
  title = 'Clustering in UMAP and Tissue Space',
  caption = 'Processed by Spaceranger 1.1\nNormilization and Clustering by Seurat'
) + plot_layout(nrow = 1)
```

I don't really like these colors so let's change them
```{r}
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
```

```{r, fig.width=10, fig.height=10, warning=FALSE}
p1 <- DimPlot(breast_cancer, reduction = "umap", label = TRUE) +
  labs(color = "Cluster") + 
  scale_color_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green"))

p2 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
  labs(fill = "Cluster")+ 
  scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green"))

p1 + p2 + plot_annotation(
  title = 'Clustering in UMAP and Tissue Space',
  caption = 'Processed by Spaceranger 1.1\nNormilization and Clustering by Seurat'
) + plot_layout(nrow = 1)
```

If interested you can also now look at UMI and Gene counts per cluster as well
```{r}
plot1 <- VlnPlot(breast_cancer, features = "nCount_Spatial", pt.size = 0.1) + 
  ggtitle("UMI") +
  scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green"))+
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(), 
        legend.position = "right") +
  NoLegend()

plot2 <- VlnPlot(breast_cancer, features = "nFeature_Spatial", pt.size = 0.1) + 
  ggtitle("Genes") +
  scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green"))+
  theme(axis.title.x = element_blank(), 
        legend.position = "right") +
  NoLegend()

plot1 + plot2
```


Now let's take a look at at a gene of interest with violin plots but also in image space.
```{r, fig.width=8, fig.height=8}
p1 <- VlnPlot(breast_cancer, features = "IGFBP5", pt.size = 0.1) + 
  ggtitle("IGFBP5") +
  scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green"))+
  theme(axis.title.x = element_blank(), 
        legend.position = "right") +
  NoLegend() +
  stat_summary(fun=mean, geom="point", shape=23, size=4, color="red")

p2 <- SpatialFeaturePlot(breast_cancer, features = "IGFBP5")+ 
  theme(legend.position = "right")

p3 <- SpatialDimPlot(breast_cancer, label = TRUE, label.size = 3) +
  labs(fill = "Cluster")+ 
  scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                               "#a65628", "#999999", "black", "pink", "purple", "brown",
                               "grey", "yellow", "green")) +
  NoLegend()

row1 <- p2 + p3 + plot_layout(nrow = 1)
row1 + p1+ plot_layout(nrow = 2, widths = c(0.5, 0.5))
```

We can also look at these data interactively
```{r eval=FALSE, include=TRUE}
LinkedDimPlot(breast_cancer)
```

## Spatially variable features

First we'll identify deferentially expressed genes. 

Parallelization helps here too let's make sure our plan is still intact

```{r, eval=FALE, include=TRUE}
plan()
```
`- call: plan("multiprocess", workers = 4)` indicates that it is

Looks like we have some very DE genes for clusters 4 and 11


clarify what ident.1 = 4, ident.2 = 6 are for
```{r, fig.width=10, warning=FALSE, message=FALSE}
de_markers <- FindMarkers(breast_cancer, ident.1 = 4, ident.2 = 6)
de_markers
```

## Identify the most up-regulated and down-regulated genes
```{r}
de_markers_up <- de_markers %>%
  tibble::rownames_to_column(var = "Gene") %>% 
  arrange(-avg_logFC)

de_markers_down <- de_markers %>%
  tibble::rownames_to_column(var = "Gene") %>% 
  arrange(avg_logFC)
```


### Most up-regulated genes
```{r, fig.width=10, fig.height=10, warning=FALSE}
SpatialFeaturePlot(object = breast_cancer, features = de_markers_up$Gene[1:10], alpha = c(0.1, 1), ncol = 3)
```

### Most down-regulated genes
```{r, fig.width=10, fig.height=10, warning=FALSE}
SpatialFeaturePlot(object = breast_cancer, features = de_markers_down$Gene[1:10], alpha = c(0.1, 1), ncol = 3)
```

what are the top variable features?
```{r}
VariableFeatures(breast_cancer)[1:10]
```

what are the top DE genes?
```{r}
rownames(de_markers)[1:10]
```

## Spatially Variable Genes

So what about spatial enrichment? 

Some methods

1. [Trendsceek](https://www.nature.com/articles/nmeth.4634)
2. [Splotch](https://www.biorxiv.org/content/10.1101/757096v1)
3. [SPARK](https://www.nature.com/articles/s41592-019-0701-7)
4. [SpatialDE](https://www.nature.com/articles/nmeth.4636)
  + We have found this implementation not to be very effective. It's also not under active development


Using the top 100 variable genes find spatially enriched ones. Note that in the Seurat Spatial Tutorial they use 1000 genes. You can also use all genes but that will take a long time. Using a calculation of [Morans I](https://en.wikipedia.org/wiki/Moran%27s_I) can sometimes be a faster approach, especially if you are using parallization.


```{r}
breast_cancer <- FindSpatiallyVariableFeatures(breast_cancer, 
                                               assay = "SCT", 
                                               slot = "scale.data", 
                                               features = VariableFeatures(breast_cancer)[1:100],
                                               selection.method = "markvariogram", verbose = TRUE)
```

Have a look at the spatially variable genes calculated by `markvariogram` ordered from most variable to least variable
```{r}
SpatiallyVariableFeatures(breast_cancer, selection.method = "markvariogram", decreasing = TRUE)
```

```{r, fig.width=10, fig.height=8, warning=FALSE}
top.features_trendseq <- head(SpatiallyVariableFeatures(breast_cancer, selection.method = "markvariogram"), 8)
SpatialFeaturePlot(breast_cancer, features = top.features_trendseq, ncol = 4, alpha = c(0.1, 1))
```

Moran's I implementation. For other spatial data types the x.cuts and y.cuts determines the grid that is laid over the tissue in the capture area. Here we'll remove those
```{r}
breast_cancer <- FindSpatiallyVariableFeatures(breast_cancer, 
                                               assay = "SCT", 
                                               slot = "scale.data", 
                                               features = VariableFeatures(breast_cancer)[1:100],
                                               selection.method = "moransi")
```


Have a look at the spatially variable genes calculated by `moransi` ordered from most variable to least variable

```{r}
SpatiallyVariableFeatures(breast_cancer, selection.method = "moransi", decreasing = TRUE)
```

```{r, fig.width=10, fig.height=8, warning=FALSE}
top.features_moransi <- head(SpatiallyVariableFeatures(breast_cancer, selection.method = "moransi"), 8)
SpatialFeaturePlot(breast_cancer, features = top.features_moransi, ncol = 4, alpha = c(0.1, 1))
```

We can see that the results are slightly different. So let's take a look at why.

```{r}
spatially_variable_genes <- breast_cancer@assays$SCT@meta.features %>%
  tidyr::drop_na()

spatially_variable_genes
```
You can see the two methods show 
```{r}
mm_cor <- cor.test(spatially_variable_genes$moransi.spatially.variable.rank, spatially_variable_genes$markvariogram.spatially.variable.rank)
ggplot(spatially_variable_genes, aes(x=moransi.spatially.variable.rank,y=markvariogram.spatially.variable.rank))+
  geom_point()+
  geom_smooth()+
  xlab("Morans I Rank")+
  ylab("Markvariogram Rank")+
  annotate("text", x = 25, y = 75, label = paste("Pearson's Correlation\n", round(mm_cor$estimate[1], digits = 2), sep = ""))+
  theme_bw()
```

## Cancer annotations

Where are these genes being expressed relative to pathologist annotation?

```{r, fig.height=8, fig.width=8}
ca <- readbitmap::read.bitmap("images/Breast Cancer Path.png")
# in the tutorial
# ca <- readbitmap::read.bitmap('/mnt/libs/shared_data/human_breast_cancer_1/images/Breast_Cancer_Path.png')
plot(0:1,0:1,type="n",ann=FALSE,axes=FALSE)
rasterImage(ca,0,0,1,1)
```

Looks like the  Matrix Gla protein ( _MGP_ ) gene is enriched in Ductal Carcinoma _In Situ_. Not a lot is known about _MGP_ in the context of cancer but it looks like it could be an interesting novel gene to investigate with regard to Ductal Carcinoma _In Situ_.

```{r, warning=FALSE}
SpatialFeaturePlot(object = breast_cancer, features = "MGP", alpha = c(0.1, 1), ncol = 3)
```

# Multiple Sections

for workshop
```{r eval=FALSE}
breast_cancer_2 <- Load10X_Spatial(data.dir = "/mnt/libs/shared_data/human_breast_cancer_2/outs/",
                filename = "V1_Breast_Cancer_Block_A_Section_2_filtered_feature_bc_matrix.h5")
```
for 10x
```{r}
breast_cancer_2 <- Load10X_Spatial(data.dir = "/mnt/analysis/marsoc/pipestances/HWHTFDSXX/SPATIAL_RNA_COUNTER_PD/163087/HEAD/outs/", 
                                   slice = "slice2")
```

```{r}
breast_cancer_merged <- merge(breast_cancer, breast_cancer_2)
```

Counts = UMI
Features = Genes
```{r, fig.width=10,fig.height=10, warning=FALSE}
plot1 <- SpatialFeaturePlot(breast_cancer_merged, features = "nCount_Spatial") + 
  theme(legend.position = "right")

plot2 <- SpatialFeaturePlot(breast_cancer_merged, features = "nFeature_Spatial") +
  theme(legend.position = "right")

plot1 + plot2 + plot_layout(nrow = 2)
```

## Normalize merged data

This will take ~3min

Default assay will now be set to SCT
```{r, warning=FALSE}
breast_cancer_merged <- SCTransform(breast_cancer_merged, assay = "Spatial", verbose = TRUE)
```

```{r, fig.width=10, warning=FALSE}
SpatialFeaturePlot(breast_cancer_merged, features = c("ERBB2", "CD8A"))
```

# Single cell/nuclei ingegration

Here we have a preprocessed Seurat object with 10k nuclei annotated from a breast cancer sample. Don't bother too much with the details of how this data was generated they don't particularly matter for our purposes. 

## Load the Seurat object
10x infrastructure
```{r}
bc_snRNA <- readRDS("/mnt/home/stephen.williams/yard/Odin/SIB_2020_Workshop/bc_snRNA.rds")
```

For the workshop
```{r eval=FALSE, include=TRUE}
bc_snRNA <- readRDS("/mnt/libs/shared_data/bc_snRNA.rds")
```


```{r}
bc_snRNA
```

```{r, fig.width=10}
DimPlot(bc_snRNA, group.by = "subclass", label = FALSE)
```
